CombIncrease_next {dfcomb}R Documentation

Combination determination with logistic model

Description

CombIncrease_next is used to determine the next or recommended combination in a phase I combination clinical trial using the design proposed by Riviere et al. entitled "A Bayesian dose-finding design for drug combination clinical trials based on the logistic model".

Usage

CombIncrease_next(ndose_a1, ndose_a2, target, target_min, target_max,
  prior_tox_a1, prior_tox_a2, cohort, final, pat_incl, dose_adm1,
  dose_adm2, tite=FALSE, toxicity, time_full=0, time_tox=0,
  time_follow=0, c_e=0.85, c_d=0.45, c_stop=0.95, c_t=0.5, c_over=0.25,
  cmin_overunder=2, cmin_mtd=3, cmin_recom=1, early_stop=1, alloc_rule=1,
  nburn=2000, niter=5000)

Arguments

ndose_a1

Number of dose levels for agent 1.

ndose_a2

Number of dose levels for agent 2.

target

Toxicity (probability) target.

target_min

Minimum of the targeted toxicity interval.

target_max

Maximum of the targeted toxicity interval.

prior_tox_a1

A vector of initial guesses of toxicity probabilities associated with the doses of agent 1. Must be of length ndose_a1.

prior_tox_a2

A vector of initial guesses of toxicity probabilities associated with the doses of agent 2. Must be of length ndose_a2.

cohort

Cohort size.

final

A boolean with value TRUE if the trial is finished and the recommended combination for further phases should be given, or FALSE (default value) if the combination determination is performed for the next cohort of patients.

pat_incl

Current number of patients included.

dose_adm1

A vector indicating the dose levels of agents 1 administered to each patient included in the trial. Must be of length pat_incl.

dose_adm2

A vector indicating the dose levels of agents 2 administered to each patient included in the trial. Must be of length pat_incl.

tite

A boolean indicating if the toxicity is considered as a time-to-event outcome (TRUE), or as a binary outcome (default value FALSE).

toxicity

A vector of observed toxicities (DLTs) for each patient included in the trial. Must be of length pat_incl. This argument is used/required only if tite=FALSE.

time_full

Full follow-up time window. This argument is used only if tite=TRUE.

time_tox

A vector of times-to-toxicity for each patient included in the trial. If no toxicity was observed for a patient, must be filled with +Inf. Must be of length pat_incl. This argument is used/required only if tite=TRUE.

time_follow

A vector of follow-up times for each patient included in the trial. Must be of length pat_incl. This argument is used/required only if tite=TRUE.

c_e

Probability threshold for dose-escalation. The default value is set at 0.85.

c_d

Probability threshold for dose-deescalation. The default value is set at 0.45.

c_stop

Probability threshold for early trial termination. The default value is set at 0.95.

c_t

Probability threshold for early trial termination for finding the MTD (see details). The default value is set at 0.5.

c_over

Probability threshold to control over-dosing (see details).

cmin_overunder

Minimum number of cohorts to be included at the lowest/highest combination before possible early trial termination for over-toxicity or under-toxicity (see details). The default value is set at 2.

cmin_mtd

Minimum number of cohorts to be included at the recommended combination before possible early trial termination for finding the MTD (see details). The default value is set at 3.

cmin_recom

Minimum number of cohorts to be included at the recommended combination at the end of the trial. The default value is set at 1.

alloc_rule

Interger (1, 2, or 3) indicating which allocation rule is used (see details). The default value is set at 1.

early_stop

Interger (1, 2, or 3) indicating which early stopping rule is used (see details). The default value is set at 1.

nburn

Number of burn-in for HMC. The default value is set at 2000.

niter

Number of iterations for HMC. The default value is set at 5000.

Details

Allocation rule:

Early stopping for over-dosing: If the current combination is the lowest (1, 1) and at least cmin_overunder cohorts have been included at that combination and P(toxicity probability at combination (i,j) > target) >= c_stop then stop the trial and do not recommend any combination.

Early stopping for under-dosing: If the current combination is the highest and at least cmin_overunder cohorts have been included at that combination and P(toxicity probability at combination (i,j) < target) >= c_stop then stop the trial and do not recommend any combination.

Early stopping for identifying the MTD:

Stopping at the maximum sample size: If the maximum sample size is reached and no stopping rule is met, then the recommended combination is the one that was tested on at least cmin_recom cohorts and with the highest posterior probability to be in the targeted interval [target_min, target_max].

Value

An object of class "CombIncrease_next" is returned, consisting of determination of the next combination and estimations. Objects generated by CombIncrease_next contain at least the following components:

n_pat_comb

Number of patients per combination.

n_tox_comb

Number of observed toxicities per combination.

pi

Estimated toxicity probabilities (if the start-up ended).

ptox_inf

Estimated probabilities that the toxicity probability is inferior to target (if the start-up ended).

ptox_inf_targ

Estimated probabilities of underdosing, i.e. to be inferior to target_min (if the start-up ended).

ptox_targ

Estimated probabilities to be in the targeted interval [target_min,target_max] (if the start-up ended).

ptox_sup_targ

Estimated probabilities of overdosing, i.e. to be superior to target_max (if the start-up ended).

(cdose1, cdose2)

NEXT RECOMMENDED COMBINATION.

inconc

Boolean indicating if trial must stop for under/over dosing.

early_conc

Boolean indicating if trial can be stopped earlier for finding the MTD.

Author(s)

Jacques-Henri Jourdan and Marie-Karelle Riviere-Jourdan eldamjh@gmail.com

References

Riviere, M-K., Yuan, Y., Dubois, F., and Zohar, S. (2014). A Bayesian dose-finding design for drug combination clinical trials based on the logistic model. Pharmaceutical Statistics.

See Also

CombIncrease_sim.

Examples

prior_a1 = c(0.12, 0.2, 0.3, 0.4, 0.5)
prior_a2 = c(0.2, 0.3, 0.4)
toxicity1 = c(0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1)
dose1 = c(1,1,1,2,2,2,3,3,3,3,3,3,3,3,3,4,4,4)
dose2 = c(1,1,1,2,2,2,3,3,3,2,2,2,1,1,1,1,1,1)
t_tox = c(rep(+Inf,8),2.9,+Inf,4.6,+Inf,+Inf,+Inf,+Inf,+Inf,+Inf,5.2)
follow = c(rep(6,15), 4.9, 3.1, 1.3)

next1 = CombIncrease_next(ndose_a1=5, ndose_a2=3, target=0.3,
  target_min=0.2, target_max=0.4, prior_tox_a1=prior_a1,
  prior_tox_a2=prior_a2, cohort=3, final=FALSE, pat_incl=18,
  dose_adm1=dose1, dose_adm2=dose2, toxicity=toxicity1, c_over=1,
  cmin_overunder=3, cmin_recom=1, early_stop=1, alloc_rule=1)
next1

next2 = CombIncrease_next(ndose_a1=5, ndose_a2=3, target=0.3,
  target_min=0.2, target_max=0.4, prior_tox_a1=prior_a1, prior_tox_a2=prior_a2,
  cohort=3, final=FALSE, pat_incl=18, dose_adm1=dose1,
  dose_adm2=dose2, tite=TRUE, time_full=6, time_tox=t_tox,
  time_follow=follow, c_over=1, cmin_overunder=3, cmin_recom=1,
  early_stop=1, alloc_rule=1)
next2

[Package dfcomb version 3.1-1 Index]